Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading mnist: 9.92MB [00:02, 3.47MB/s]                            
Extracting mnist: 100%|██████████| 60.0K/60.0K [00:10<00:00, 5.51KFile/s]
Downloading celeba: 1.44GB [02:15, 10.6MB/s]                               
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f4eec0a1588>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f4ee7d42518>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    real_input_images = tf.placeholder(tf.float32,(None, image_width,image_height,image_channels))
    z_input = tf.placeholder(tf.float32,(None, z_dim))
    learning_rate = tf.placeholder(tf.float32)
    
    return real_input_images, z_input, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [7]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    leaky_relu_alpha=0.2
    with tf.variable_scope('discriminator', reuse=reuse):

        #print("images shape: ", images.shape)
        # Input layer is 28x28x3
        
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        leaky_relu1 = tf.maximum(leaky_relu_alpha * x1, x1)
        #print("leaky_relu1 shape: ", leaky_relu1.shape)
        # 14x14x64
        
        x2 = tf.layers.conv2d(leaky_relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(leaky_relu_alpha * bn2, bn2)
        #print("relu2 shape: ", relu2.shape)
        # 7x7x128
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(leaky_relu_alpha * bn3, bn3)
        #print("relu3 shape: ", relu3.shape)
        # 4x4x256

        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [8]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    leaky_relu_alpha=0.2
    
    #reuse the variables if not training
    with tf.variable_scope('generator', reuse=not is_train):
        
        #print("z shape: ", z.shape )
        
        # First fully connected layer
        x1 = tf.layers.dense(z, 7*7*256)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 256))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(leaky_relu_alpha * x1, x1)
        #print("x1 shape: ", x1.shape)
        # 7x7x256 now
        
        # Using a stride of one to preserve the width and height at this step
        x2 = tf.layers.conv2d_transpose(x1, 128, 5, strides=1, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(leaky_relu_alpha * x2, x2)
        #print("x2 shape: ", x2.shape)
        # 7x7x128 now
        
        x3 = tf.layers.conv2d_transpose(x2, 64, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(leaky_relu_alpha * x3, x3)
        #print("x3 shape: " , x3.shape);
        # 14x14x64 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
        #print("logits shape: ", logits.shape)
        # 28x28x5 now
        
        out = tf.tanh(logits)
        
        return out
    return None


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    # use smoothing for the real loss
    smoothing = 1 - 0.1
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*smoothing))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [11]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [12]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [16]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    print("Data shape: ", data_shape)
    # get input dimensions
    _, image_width, image_height, image_channels=data_shape
    
    # get placeholders
    input_real, input_z, learning_rate_placeholder = model_inputs(image_width, image_height, image_channels, z_dim)
    
    # get loss functions
    d_loss, g_loss = model_loss(input_real, input_z, image_channels)
    
    # get the optimizers
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    

    
    print_every = 10
    show_every = 100
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            
            # count the number of batches per epoch
            number_of_batches_done = 0    
                
            for batch_images in get_batches(batch_size):
                
                number_of_batches_done += 1
                
                #print("Batch images: ", batch_images)
                
                # make sure the image values are between -1 and 1
                # as the values are between -0.5 and 0.5 multiply by 2 to ensure the wanted value range.
                batch_images=batch_images*2
                
                 # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images})

                if number_of_batches_done % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Batch {}...".format(number_of_batches_done),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    # Save losses to view after training
                    #losses.append((train_loss_d, train_loss_g))

                if number_of_batches_done % show_every == 0:
                    show_generator_output(sess, n_images=25, input_z=input_z, out_channel_dim=image_channels, image_mode=data_image_mode)

            
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [21]:
batch_size = 32
z_dim = 100
learning_rate = 0.01
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Data shape:  (60000, 28, 28, 1)
Epoch 1/2... Batch 10... Discriminator Loss: 2.8866... Generator Loss: 2.1050
Epoch 1/2... Batch 20... Discriminator Loss: 3.3541... Generator Loss: 5.7878
Epoch 1/2... Batch 30... Discriminator Loss: 2.9798... Generator Loss: 0.0999
Epoch 1/2... Batch 40... Discriminator Loss: 2.7890... Generator Loss: 0.1737
Epoch 1/2... Batch 50... Discriminator Loss: 2.6321... Generator Loss: 0.1963
Epoch 1/2... Batch 60... Discriminator Loss: 2.3720... Generator Loss: 6.6174
Epoch 1/2... Batch 70... Discriminator Loss: 5.8675... Generator Loss: 8.8961
Epoch 1/2... Batch 80... Discriminator Loss: 3.5169... Generator Loss: 0.1109
Epoch 1/2... Batch 90... Discriminator Loss: 2.3359... Generator Loss: 1.8098
Epoch 1/2... Batch 100... Discriminator Loss: 1.9111... Generator Loss: 3.1701
Epoch 1/2... Batch 110... Discriminator Loss: 0.5616... Generator Loss: 2.6442
Epoch 1/2... Batch 120... Discriminator Loss: 1.4415... Generator Loss: 1.0480
Epoch 1/2... Batch 130... Discriminator Loss: 1.2716... Generator Loss: 2.0193
Epoch 1/2... Batch 140... Discriminator Loss: 4.2364... Generator Loss: 0.0670
Epoch 1/2... Batch 150... Discriminator Loss: 3.5374... Generator Loss: 4.2868
Epoch 1/2... Batch 160... Discriminator Loss: 1.7314... Generator Loss: 3.9843
Epoch 1/2... Batch 170... Discriminator Loss: 1.4008... Generator Loss: 0.6427
Epoch 1/2... Batch 180... Discriminator Loss: 0.8271... Generator Loss: 1.6079
Epoch 1/2... Batch 190... Discriminator Loss: 1.2690... Generator Loss: 2.0028
Epoch 1/2... Batch 200... Discriminator Loss: 1.1701... Generator Loss: 0.9946
Epoch 1/2... Batch 210... Discriminator Loss: 0.8593... Generator Loss: 1.2460
Epoch 1/2... Batch 220... Discriminator Loss: 1.3867... Generator Loss: 0.5892
Epoch 1/2... Batch 230... Discriminator Loss: 1.3135... Generator Loss: 0.9647
Epoch 1/2... Batch 240... Discriminator Loss: 1.5303... Generator Loss: 0.5389
Epoch 1/2... Batch 250... Discriminator Loss: 1.3564... Generator Loss: 1.3401
Epoch 1/2... Batch 260... Discriminator Loss: 1.0456... Generator Loss: 1.3146
Epoch 1/2... Batch 270... Discriminator Loss: 1.4230... Generator Loss: 0.6680
Epoch 1/2... Batch 280... Discriminator Loss: 1.7714... Generator Loss: 1.2250
Epoch 1/2... Batch 290... Discriminator Loss: 0.9899... Generator Loss: 1.4496
Epoch 1/2... Batch 300... Discriminator Loss: 1.7463... Generator Loss: 0.9099
Epoch 1/2... Batch 310... Discriminator Loss: 1.9987... Generator Loss: 0.2667
Epoch 1/2... Batch 320... Discriminator Loss: 1.1933... Generator Loss: 1.0297
Epoch 1/2... Batch 330... Discriminator Loss: 1.1615... Generator Loss: 1.1255
Epoch 1/2... Batch 340... Discriminator Loss: 1.4582... Generator Loss: 0.4919
Epoch 1/2... Batch 350... Discriminator Loss: 1.2233... Generator Loss: 0.6966
Epoch 1/2... Batch 360... Discriminator Loss: 1.0710... Generator Loss: 1.1995
Epoch 1/2... Batch 370... Discriminator Loss: 1.2190... Generator Loss: 0.7990
Epoch 1/2... Batch 380... Discriminator Loss: 1.4844... Generator Loss: 0.4926
Epoch 1/2... Batch 390... Discriminator Loss: 1.3124... Generator Loss: 1.1735
Epoch 1/2... Batch 400... Discriminator Loss: 1.4859... Generator Loss: 0.6717
Epoch 1/2... Batch 410... Discriminator Loss: 1.1192... Generator Loss: 1.7012
Epoch 1/2... Batch 420... Discriminator Loss: 1.1981... Generator Loss: 1.0109
Epoch 1/2... Batch 430... Discriminator Loss: 2.7712... Generator Loss: 3.6539
Epoch 1/2... Batch 440... Discriminator Loss: 1.1791... Generator Loss: 1.0063
Epoch 1/2... Batch 450... Discriminator Loss: 1.1994... Generator Loss: 1.8113
Epoch 1/2... Batch 460... Discriminator Loss: 1.2252... Generator Loss: 1.3493
Epoch 1/2... Batch 470... Discriminator Loss: 1.2621... Generator Loss: 1.7449
Epoch 1/2... Batch 480... Discriminator Loss: 1.2503... Generator Loss: 0.6694
Epoch 1/2... Batch 490... Discriminator Loss: 1.4538... Generator Loss: 1.4284
Epoch 1/2... Batch 500... Discriminator Loss: 1.3558... Generator Loss: 0.5825
Epoch 1/2... Batch 510... Discriminator Loss: 0.9261... Generator Loss: 1.2236
Epoch 1/2... Batch 520... Discriminator Loss: 1.2577... Generator Loss: 0.9494
Epoch 1/2... Batch 530... Discriminator Loss: 1.3535... Generator Loss: 0.8329
Epoch 1/2... Batch 540... Discriminator Loss: 1.0696... Generator Loss: 0.9751
Epoch 1/2... Batch 550... Discriminator Loss: 1.0134... Generator Loss: 1.2055
Epoch 1/2... Batch 560... Discriminator Loss: 1.1929... Generator Loss: 0.8451
Epoch 1/2... Batch 570... Discriminator Loss: 1.2663... Generator Loss: 0.7976
Epoch 1/2... Batch 580... Discriminator Loss: 1.1631... Generator Loss: 1.1632
Epoch 1/2... Batch 590... Discriminator Loss: 1.0459... Generator Loss: 0.9545
Epoch 1/2... Batch 600... Discriminator Loss: 1.3093... Generator Loss: 0.6889
Epoch 1/2... Batch 610... Discriminator Loss: 1.1907... Generator Loss: 0.6707
Epoch 1/2... Batch 620... Discriminator Loss: 1.2792... Generator Loss: 1.7829
Epoch 1/2... Batch 630... Discriminator Loss: 1.1704... Generator Loss: 0.7668
Epoch 1/2... Batch 640... Discriminator Loss: 1.2360... Generator Loss: 0.6585
Epoch 1/2... Batch 650... Discriminator Loss: 1.2437... Generator Loss: 0.8339
Epoch 1/2... Batch 660... Discriminator Loss: 1.7029... Generator Loss: 0.3996
Epoch 1/2... Batch 670... Discriminator Loss: 1.2549... Generator Loss: 0.7615
Epoch 1/2... Batch 680... Discriminator Loss: 1.5104... Generator Loss: 0.6553
Epoch 1/2... Batch 690... Discriminator Loss: 1.2982... Generator Loss: 0.6506
Epoch 1/2... Batch 700... Discriminator Loss: 1.2237... Generator Loss: 0.9910
Epoch 1/2... Batch 710... Discriminator Loss: 1.5275... Generator Loss: 0.5111
Epoch 1/2... Batch 720... Discriminator Loss: 1.1848... Generator Loss: 0.7423
Epoch 1/2... Batch 730... Discriminator Loss: 1.3675... Generator Loss: 0.6465
Epoch 1/2... Batch 740... Discriminator Loss: 1.6934... Generator Loss: 0.4142
Epoch 1/2... Batch 750... Discriminator Loss: 1.0250... Generator Loss: 0.9643
Epoch 1/2... Batch 760... Discriminator Loss: 1.1375... Generator Loss: 0.8281
Epoch 1/2... Batch 770... Discriminator Loss: 1.0684... Generator Loss: 1.0841
Epoch 1/2... Batch 780... Discriminator Loss: 1.3254... Generator Loss: 1.3081
Epoch 1/2... Batch 790... Discriminator Loss: 1.0964... Generator Loss: 1.7505
Epoch 1/2... Batch 800... Discriminator Loss: 1.1807... Generator Loss: 1.8069
Epoch 1/2... Batch 810... Discriminator Loss: 1.2969... Generator Loss: 1.4345
Epoch 1/2... Batch 820... Discriminator Loss: 1.0020... Generator Loss: 1.0615
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Epoch 2/2... Batch 1590... Discriminator Loss: 1.1330... Generator Loss: 0.8490
Epoch 2/2... Batch 1600... Discriminator Loss: 0.9403... Generator Loss: 1.0733
Epoch 2/2... Batch 1610... Discriminator Loss: 2.1906... Generator Loss: 0.2757
Epoch 2/2... Batch 1620... Discriminator Loss: 0.9386... Generator Loss: 4.1602
Epoch 2/2... Batch 1630... Discriminator Loss: 0.8205... Generator Loss: 1.3117
Epoch 2/2... Batch 1640... Discriminator Loss: 0.9425... Generator Loss: 1.0370
Epoch 2/2... Batch 1650... Discriminator Loss: 0.6760... Generator Loss: 2.7489
Epoch 2/2... Batch 1660... Discriminator Loss: 0.6849... Generator Loss: 1.9656
Epoch 2/2... Batch 1670... Discriminator Loss: 0.7130... Generator Loss: 1.6038
Epoch 2/2... Batch 1680... Discriminator Loss: 0.9257... Generator Loss: 2.6006
Epoch 2/2... Batch 1690... Discriminator Loss: 1.8863... Generator Loss: 0.3843
Epoch 2/2... Batch 1700... Discriminator Loss: 1.1355... Generator Loss: 0.8028
Epoch 2/2... Batch 1710... Discriminator Loss: 1.7686... Generator Loss: 4.7757
Epoch 2/2... Batch 1720... Discriminator Loss: 0.7434... Generator Loss: 1.6462
Epoch 2/2... Batch 1730... Discriminator Loss: 1.0494... Generator Loss: 0.8864
Epoch 2/2... Batch 1740... Discriminator Loss: 0.9376... Generator Loss: 1.1612
Epoch 2/2... Batch 1750... Discriminator Loss: 1.2622... Generator Loss: 0.8044
Epoch 2/2... Batch 1760... Discriminator Loss: 0.7206... Generator Loss: 1.5492
Epoch 2/2... Batch 1770... Discriminator Loss: 0.6798... Generator Loss: 1.6313
Epoch 2/2... Batch 1780... Discriminator Loss: 0.8478... Generator Loss: 1.3102
Epoch 2/2... Batch 1790... Discriminator Loss: 1.2314... Generator Loss: 0.6513
Epoch 2/2... Batch 1800... Discriminator Loss: 1.6287... Generator Loss: 0.6039
Epoch 2/2... Batch 1810... Discriminator Loss: 0.7988... Generator Loss: 1.9624
Epoch 2/2... Batch 1820... Discriminator Loss: 1.8294... Generator Loss: 0.4084
Epoch 2/2... Batch 1830... Discriminator Loss: 1.8448... Generator Loss: 0.3853
Epoch 2/2... Batch 1840... Discriminator Loss: 0.6257... Generator Loss: 1.8470
Epoch 2/2... Batch 1850... Discriminator Loss: 0.7396... Generator Loss: 1.4091
Epoch 2/2... Batch 1860... Discriminator Loss: 0.7373... Generator Loss: 1.4147
Epoch 2/2... Batch 1870... Discriminator Loss: 0.9776... Generator Loss: 1.0223

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 32
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Data shape:  (202599, 28, 28, 3)
Epoch 1/1... Batch 10... Discriminator Loss: 1.9639... Generator Loss: 8.4855
Epoch 1/1... Batch 20... Discriminator Loss: 4.6842... Generator Loss: 0.0601
Epoch 1/1... Batch 30... Discriminator Loss: 1.5228... Generator Loss: 1.3465
Epoch 1/1... Batch 40... Discriminator Loss: 0.7839... Generator Loss: 1.8320
Epoch 1/1... Batch 50... Discriminator Loss: 1.2984... Generator Loss: 0.6939
Epoch 1/1... Batch 60... Discriminator Loss: 0.7020... Generator Loss: 2.4768
Epoch 1/1... Batch 70... Discriminator Loss: 1.7453... Generator Loss: 0.4788
Epoch 1/1... Batch 80... Discriminator Loss: 0.9200... Generator Loss: 1.7436
Epoch 1/1... Batch 90... Discriminator Loss: 1.4724... Generator Loss: 0.7801
Epoch 1/1... Batch 100... Discriminator Loss: 1.0908... Generator Loss: 1.5505
Epoch 1/1... Batch 110... Discriminator Loss: 2.4652... Generator Loss: 0.2696
Epoch 1/1... Batch 120... Discriminator Loss: 0.9626... Generator Loss: 2.1402
Epoch 1/1... Batch 130... Discriminator Loss: 1.2133... Generator Loss: 0.7924
Epoch 1/1... Batch 140... Discriminator Loss: 1.1295... Generator Loss: 2.0732
Epoch 1/1... Batch 150... Discriminator Loss: 1.1070... Generator Loss: 1.3684
Epoch 1/1... Batch 160... Discriminator Loss: 1.2760... Generator Loss: 0.6901
Epoch 1/1... Batch 170... Discriminator Loss: 1.1400... Generator Loss: 0.9205
Epoch 1/1... Batch 180... Discriminator Loss: 1.3104... Generator Loss: 0.6418
Epoch 1/1... Batch 190... Discriminator Loss: 1.3894... Generator Loss: 0.5815
Epoch 1/1... Batch 200... Discriminator Loss: 1.1157... Generator Loss: 0.8915
Epoch 1/1... Batch 210... Discriminator Loss: 1.2807... Generator Loss: 0.6909
Epoch 1/1... Batch 220... Discriminator Loss: 1.3276... Generator Loss: 0.9254
Epoch 1/1... Batch 230... Discriminator Loss: 1.2250... Generator Loss: 1.2847
Epoch 1/1... Batch 240... Discriminator Loss: 1.1976... Generator Loss: 0.8688
Epoch 1/1... Batch 250... Discriminator Loss: 1.1504... Generator Loss: 0.7906
Epoch 1/1... Batch 260... Discriminator Loss: 1.3348... Generator Loss: 0.5959
Epoch 1/1... Batch 270... Discriminator Loss: 1.5399... Generator Loss: 2.0837
Epoch 1/1... Batch 280... Discriminator Loss: 0.9250... Generator Loss: 1.0743
Epoch 1/1... Batch 290... Discriminator Loss: 1.3297... Generator Loss: 1.3585
Epoch 1/1... Batch 300... Discriminator Loss: 1.1029... Generator Loss: 1.0810
Epoch 1/1... Batch 310... Discriminator Loss: 1.0573... Generator Loss: 0.7975
Epoch 1/1... Batch 320... Discriminator Loss: 1.1089... Generator Loss: 1.0423
Epoch 1/1... Batch 330... Discriminator Loss: 1.4638... Generator Loss: 0.7179
Epoch 1/1... Batch 340... Discriminator Loss: 1.5986... Generator Loss: 0.4446
Epoch 1/1... Batch 350... Discriminator Loss: 1.8595... Generator Loss: 0.6207
Epoch 1/1... Batch 360... Discriminator Loss: 1.2651... Generator Loss: 1.1057
Epoch 1/1... Batch 370... Discriminator Loss: 1.1544... Generator Loss: 1.2573
Epoch 1/1... Batch 380... Discriminator Loss: 1.0093... Generator Loss: 0.8930
Epoch 1/1... Batch 390... Discriminator Loss: 0.8520... Generator Loss: 1.2199
Epoch 1/1... Batch 400... Discriminator Loss: 1.1552... Generator Loss: 2.0184
Epoch 1/1... Batch 410... Discriminator Loss: 1.3754... Generator Loss: 0.8338
Epoch 1/1... Batch 420... Discriminator Loss: 1.3450... Generator Loss: 0.5794
Epoch 1/1... Batch 430... Discriminator Loss: 1.0568... Generator Loss: 1.4505
Epoch 1/1... Batch 440... Discriminator Loss: 1.1675... Generator Loss: 0.9594
Epoch 1/1... Batch 450... Discriminator Loss: 1.2890... Generator Loss: 0.6883
Epoch 1/1... Batch 460... Discriminator Loss: 1.1563... Generator Loss: 1.0402
Epoch 1/1... Batch 470... Discriminator Loss: 1.1226... Generator Loss: 0.8345
Epoch 1/1... Batch 480... Discriminator Loss: 1.6026... Generator Loss: 0.6151
Epoch 1/1... Batch 490... Discriminator Loss: 1.8431... Generator Loss: 0.4083
Epoch 1/1... Batch 500... Discriminator Loss: 1.2191... Generator Loss: 1.0483
Epoch 1/1... Batch 510... Discriminator Loss: 1.2643... Generator Loss: 0.6776
Epoch 1/1... Batch 520... Discriminator Loss: 1.3636... Generator Loss: 0.8496
Epoch 1/1... Batch 530... Discriminator Loss: 1.4515... Generator Loss: 0.8704
Epoch 1/1... Batch 540... Discriminator Loss: 1.1732... Generator Loss: 0.9441
Epoch 1/1... Batch 550... Discriminator Loss: 1.3262... Generator Loss: 0.7379
Epoch 1/1... Batch 560... Discriminator Loss: 1.2716... Generator Loss: 1.1307
Epoch 1/1... Batch 570... Discriminator Loss: 1.5557... Generator Loss: 0.5799
Epoch 1/1... Batch 580... Discriminator Loss: 1.2610... Generator Loss: 0.8616
Epoch 1/1... Batch 590... Discriminator Loss: 1.3056... Generator Loss: 0.6994
Epoch 1/1... Batch 600... Discriminator Loss: 1.1763... Generator Loss: 1.2336
Epoch 1/1... Batch 610... Discriminator Loss: 1.5479... Generator Loss: 0.5695
Epoch 1/1... Batch 620... Discriminator Loss: 1.2213... Generator Loss: 0.8351
Epoch 1/1... Batch 630... Discriminator Loss: 1.6282... Generator Loss: 0.6411
Epoch 1/1... Batch 640... Discriminator Loss: 1.2633... Generator Loss: 1.0315
Epoch 1/1... Batch 650... Discriminator Loss: 1.5360... Generator Loss: 0.6146
Epoch 1/1... Batch 660... Discriminator Loss: 1.1079... Generator Loss: 1.0561
Epoch 1/1... Batch 670... Discriminator Loss: 1.7289... Generator Loss: 1.5813
Epoch 1/1... Batch 680... Discriminator Loss: 1.4733... Generator Loss: 0.5458
Epoch 1/1... Batch 690... Discriminator Loss: 1.2805... Generator Loss: 1.7442
Epoch 1/1... Batch 700... Discriminator Loss: 1.3089... Generator Loss: 1.0114
Epoch 1/1... Batch 710... Discriminator Loss: 1.1709... Generator Loss: 0.7811
Epoch 1/1... Batch 720... Discriminator Loss: 1.4121... Generator Loss: 0.8779
Epoch 1/1... Batch 730... Discriminator Loss: 0.9079... Generator Loss: 1.1179
Epoch 1/1... Batch 740... Discriminator Loss: 1.4003... Generator Loss: 1.2767
Epoch 1/1... Batch 750... Discriminator Loss: 1.1181... Generator Loss: 1.1831
Epoch 1/1... Batch 760... Discriminator Loss: 1.2287... Generator Loss: 0.8243
Epoch 1/1... Batch 770... Discriminator Loss: 1.2638... Generator Loss: 0.9652
Epoch 1/1... Batch 780... Discriminator Loss: 1.3680... Generator Loss: 0.7607
Epoch 1/1... Batch 790... Discriminator Loss: 1.2518... Generator Loss: 0.9774
Epoch 1/1... Batch 800... Discriminator Loss: 1.0881... Generator Loss: 0.9347
Epoch 1/1... Batch 810... Discriminator Loss: 1.3475... Generator Loss: 0.6617
Epoch 1/1... Batch 820... Discriminator Loss: 1.5311... Generator Loss: 1.9289
Epoch 1/1... Batch 830... Discriminator Loss: 1.4130... Generator Loss: 0.7098
Epoch 1/1... Batch 840... Discriminator Loss: 1.2395... Generator Loss: 0.8409
Epoch 1/1... Batch 850... Discriminator Loss: 1.3921... Generator Loss: 0.6037
Epoch 1/1... Batch 860... Discriminator Loss: 1.0748... Generator Loss: 1.0677
Epoch 1/1... Batch 870... Discriminator Loss: 1.7004... Generator Loss: 1.6644
Epoch 1/1... Batch 880... Discriminator Loss: 1.2016... Generator Loss: 1.0125
Epoch 1/1... Batch 890... Discriminator Loss: 1.3125... Generator Loss: 0.9471
Epoch 1/1... Batch 900... Discriminator Loss: 1.2668... Generator Loss: 0.7595
Epoch 1/1... Batch 910... Discriminator Loss: 1.2659... Generator Loss: 0.9880
Epoch 1/1... Batch 920... Discriminator Loss: 1.0812... Generator Loss: 1.0047
Epoch 1/1... Batch 930... Discriminator Loss: 1.2644... Generator Loss: 0.7075
Epoch 1/1... Batch 940... Discriminator Loss: 1.2650... Generator Loss: 0.7694
Epoch 1/1... Batch 950... Discriminator Loss: 0.9444... Generator Loss: 0.9808
Epoch 1/1... Batch 960... Discriminator Loss: 1.6928... Generator Loss: 1.6912
Epoch 1/1... Batch 970... Discriminator Loss: 1.1776... Generator Loss: 0.9888
Epoch 1/1... Batch 980... Discriminator Loss: 1.1922... Generator Loss: 0.7728
Epoch 1/1... Batch 990... Discriminator Loss: 1.4185... Generator Loss: 0.7742
Epoch 1/1... Batch 1000... Discriminator Loss: 1.1001... Generator Loss: 0.9167
Epoch 1/1... Batch 1010... Discriminator Loss: 1.3582... Generator Loss: 0.7858
Epoch 1/1... Batch 1020... Discriminator Loss: 1.2401... Generator Loss: 0.8554
Epoch 1/1... Batch 1030... Discriminator Loss: 1.3594... Generator Loss: 0.6083
Epoch 1/1... Batch 1040... Discriminator Loss: 1.5666... Generator Loss: 0.6158
Epoch 1/1... Batch 1050... Discriminator Loss: 1.3082... Generator Loss: 0.7725
Epoch 1/1... Batch 1060... Discriminator Loss: 1.1291... Generator Loss: 1.0127
Epoch 1/1... Batch 1070... Discriminator Loss: 1.3279... Generator Loss: 0.7828
Epoch 1/1... Batch 1080... Discriminator Loss: 1.3629... Generator Loss: 1.1690
Epoch 1/1... Batch 1090... Discriminator Loss: 1.0944... Generator Loss: 0.9942
Epoch 1/1... Batch 1100... Discriminator Loss: 1.4366... Generator Loss: 0.6472
Epoch 1/1... Batch 1110... Discriminator Loss: 1.2971... Generator Loss: 0.5503
Epoch 1/1... Batch 1120... Discriminator Loss: 1.5391... Generator Loss: 0.7375
Epoch 1/1... Batch 1130... Discriminator Loss: 1.2530... Generator Loss: 0.6897
Epoch 1/1... Batch 1140... Discriminator Loss: 1.3840... Generator Loss: 1.3625
Epoch 1/1... Batch 1150... Discriminator Loss: 1.3150... Generator Loss: 0.8305
Epoch 1/1... Batch 1160... Discriminator Loss: 1.3125... Generator Loss: 0.8664
Epoch 1/1... Batch 1170... Discriminator Loss: 1.3931... Generator Loss: 0.6149
Epoch 1/1... Batch 1180... Discriminator Loss: 1.2894... Generator Loss: 1.2028
Epoch 1/1... Batch 1190... Discriminator Loss: 1.2571... Generator Loss: 0.9310
Epoch 1/1... Batch 1200... Discriminator Loss: 1.2818... Generator Loss: 0.9110
Epoch 1/1... Batch 1210... Discriminator Loss: 1.1551... Generator Loss: 0.9735
Epoch 1/1... Batch 1220... Discriminator Loss: 1.3469... Generator Loss: 0.6194
Epoch 1/1... Batch 1230... Discriminator Loss: 1.4711... Generator Loss: 0.8583
Epoch 1/1... Batch 1240... Discriminator Loss: 1.1454... Generator Loss: 1.6159
Epoch 1/1... Batch 1250... Discriminator Loss: 1.4755... Generator Loss: 0.7012
Epoch 1/1... Batch 1260... Discriminator Loss: 1.3557... Generator Loss: 0.6962
Epoch 1/1... Batch 1270... Discriminator Loss: 1.2807... Generator Loss: 1.0284
Epoch 1/1... Batch 1280... Discriminator Loss: 1.2852... Generator Loss: 1.2183
Epoch 1/1... Batch 1290... Discriminator Loss: 1.2684... Generator Loss: 0.8045
Epoch 1/1... Batch 1300... Discriminator Loss: 1.2085... Generator Loss: 0.9915
Epoch 1/1... Batch 1310... Discriminator Loss: 1.2544... Generator Loss: 0.8401
Epoch 1/1... Batch 1320... Discriminator Loss: 1.2274... Generator Loss: 1.4325
Epoch 1/1... Batch 1330... Discriminator Loss: 1.3832... Generator Loss: 0.6084
Epoch 1/1... Batch 1340... Discriminator Loss: 1.1455... Generator Loss: 0.7950
Epoch 1/1... Batch 1350... Discriminator Loss: 1.2825... Generator Loss: 0.9840
Epoch 1/1... Batch 1360... Discriminator Loss: 1.2150... Generator Loss: 0.7248
Epoch 1/1... Batch 1370... Discriminator Loss: 1.2206... Generator Loss: 0.7212
Epoch 1/1... Batch 1380... Discriminator Loss: 1.1935... Generator Loss: 0.6367
Epoch 1/1... Batch 1390... Discriminator Loss: 1.4294... Generator Loss: 0.6447
Epoch 1/1... Batch 1400... Discriminator Loss: 1.2938... Generator Loss: 0.9485
Epoch 1/1... Batch 1410... Discriminator Loss: 1.4460... Generator Loss: 0.8914
Epoch 1/1... Batch 1420... Discriminator Loss: 1.4499... Generator Loss: 0.5199
Epoch 1/1... Batch 1430... Discriminator Loss: 1.1531... Generator Loss: 0.7206
Epoch 1/1... Batch 1440... Discriminator Loss: 1.3059... Generator Loss: 0.5986
Epoch 1/1... Batch 1450... Discriminator Loss: 1.2654... Generator Loss: 0.9296
Epoch 1/1... Batch 1460... Discriminator Loss: 1.0927... Generator Loss: 0.7939
Epoch 1/1... Batch 1470... Discriminator Loss: 1.2347... Generator Loss: 1.1205
Epoch 1/1... Batch 1480... Discriminator Loss: 1.4230... Generator Loss: 0.5114
Epoch 1/1... Batch 1490... Discriminator Loss: 1.3861... Generator Loss: 0.5457
Epoch 1/1... Batch 1500... Discriminator Loss: 1.3645... Generator Loss: 0.6387
Epoch 1/1... Batch 1510... Discriminator Loss: 1.1290... Generator Loss: 1.0073
Epoch 1/1... Batch 1520... Discriminator Loss: 1.2669... Generator Loss: 0.9070
Epoch 1/1... Batch 1530... Discriminator Loss: 1.3045... Generator Loss: 0.6715
Epoch 1/1... Batch 1540... Discriminator Loss: 1.2145... Generator Loss: 0.9681
Epoch 1/1... Batch 1550... Discriminator Loss: 1.1347... Generator Loss: 0.9206
Epoch 1/1... Batch 1560... Discriminator Loss: 1.3711... Generator Loss: 1.3187
Epoch 1/1... Batch 1570... Discriminator Loss: 1.2677... Generator Loss: 1.2531
Epoch 1/1... Batch 1580... Discriminator Loss: 1.3089... Generator Loss: 1.0182
Epoch 1/1... Batch 1590... Discriminator Loss: 1.1121... Generator Loss: 0.9205
Epoch 1/1... Batch 1600... Discriminator Loss: 1.4876... Generator Loss: 0.8488
Epoch 1/1... Batch 1610... Discriminator Loss: 1.2065... Generator Loss: 0.8642
Epoch 1/1... Batch 1620... Discriminator Loss: 1.1619... Generator Loss: 0.8493
Epoch 1/1... Batch 1630... Discriminator Loss: 1.5670... Generator Loss: 0.3989
Epoch 1/1... Batch 1640... Discriminator Loss: 1.1613... Generator Loss: 0.9304
Epoch 1/1... Batch 1650... Discriminator Loss: 1.4169... Generator Loss: 0.7216
Epoch 1/1... Batch 1660... Discriminator Loss: 1.1884... Generator Loss: 0.7284
Epoch 1/1... Batch 1670... Discriminator Loss: 1.3217... Generator Loss: 0.8613
Epoch 1/1... Batch 1680... Discriminator Loss: 1.2744... Generator Loss: 1.3634
Epoch 1/1... Batch 1690... Discriminator Loss: 1.1955... Generator Loss: 0.9960
Epoch 1/1... Batch 1700... Discriminator Loss: 1.2732... Generator Loss: 0.8229
Epoch 1/1... Batch 1710... Discriminator Loss: 1.3950... Generator Loss: 0.5333
Epoch 1/1... Batch 1720... Discriminator Loss: 1.3460... Generator Loss: 0.8323
Epoch 1/1... Batch 1730... Discriminator Loss: 1.3120... Generator Loss: 0.6770
Epoch 1/1... Batch 1740... Discriminator Loss: 1.0543... Generator Loss: 1.1942
Epoch 1/1... Batch 1750... Discriminator Loss: 1.6037... Generator Loss: 0.3995
Epoch 1/1... Batch 1760... Discriminator Loss: 1.1599... Generator Loss: 0.8186
Epoch 1/1... Batch 1770... Discriminator Loss: 1.1550... Generator Loss: 0.9610
Epoch 1/1... Batch 1780... Discriminator Loss: 1.3238... Generator Loss: 1.0263
Epoch 1/1... Batch 1790... Discriminator Loss: 1.2946... Generator Loss: 1.0685
Epoch 1/1... Batch 1800... Discriminator Loss: 1.2256... Generator Loss: 0.8812
Epoch 1/1... Batch 1810... Discriminator Loss: 1.9776... Generator Loss: 0.2657
Epoch 1/1... Batch 1820... Discriminator Loss: 1.4854... Generator Loss: 0.5416
Epoch 1/1... Batch 1830... Discriminator Loss: 1.2686... Generator Loss: 1.0845
Epoch 1/1... Batch 1840... Discriminator Loss: 1.6173... Generator Loss: 0.3729
Epoch 1/1... Batch 1850... Discriminator Loss: 1.3900... Generator Loss: 0.8407
Epoch 1/1... Batch 1860... Discriminator Loss: 1.2012... Generator Loss: 0.7842
Epoch 1/1... Batch 1870... Discriminator Loss: 1.6380... Generator Loss: 0.4497
Epoch 1/1... Batch 1880... Discriminator Loss: 1.3346... Generator Loss: 0.8086
Epoch 1/1... Batch 1890... Discriminator Loss: 1.3751... Generator Loss: 0.5879
Epoch 1/1... Batch 1900... Discriminator Loss: 1.1478... Generator Loss: 0.9874
Epoch 1/1... Batch 1910... Discriminator Loss: 1.5350... Generator Loss: 1.6448
Epoch 1/1... Batch 1920... Discriminator Loss: 1.3442... Generator Loss: 0.8218
Epoch 1/1... Batch 1930... Discriminator Loss: 1.1829... Generator Loss: 1.0252
Epoch 1/1... Batch 1940... Discriminator Loss: 1.2499... Generator Loss: 0.9091
Epoch 1/1... Batch 1950... Discriminator Loss: 1.4370... Generator Loss: 0.5181
Epoch 1/1... Batch 1960... Discriminator Loss: 1.3248... Generator Loss: 0.9811
Epoch 1/1... Batch 1970... Discriminator Loss: 1.2699... Generator Loss: 0.9612
Epoch 1/1... Batch 1980... Discriminator Loss: 1.4070... Generator Loss: 0.5791
Epoch 1/1... Batch 1990... Discriminator Loss: 1.2995... Generator Loss: 0.6283
Epoch 1/1... Batch 2000... Discriminator Loss: 1.3530... Generator Loss: 0.7585
Epoch 1/1... Batch 2010... Discriminator Loss: 1.3635... Generator Loss: 0.6118
Epoch 1/1... Batch 2020... Discriminator Loss: 1.4021... Generator Loss: 0.6908
Epoch 1/1... Batch 2030... Discriminator Loss: 1.1386... Generator Loss: 1.2249
Epoch 1/1... Batch 2040... Discriminator Loss: 1.2938... Generator Loss: 0.6754
Epoch 1/1... Batch 2050... Discriminator Loss: 1.2208... Generator Loss: 1.1649
Epoch 1/1... Batch 2060... Discriminator Loss: 1.0103... Generator Loss: 0.8716
Epoch 1/1... Batch 2070... Discriminator Loss: 1.2067... Generator Loss: 0.8211
Epoch 1/1... Batch 2080... Discriminator Loss: 1.2715... Generator Loss: 0.7697
Epoch 1/1... Batch 2090... Discriminator Loss: 1.2221... Generator Loss: 1.1174
Epoch 1/1... Batch 2100... Discriminator Loss: 1.3082... Generator Loss: 1.0233
Epoch 1/1... Batch 2110... Discriminator Loss: 1.5653... Generator Loss: 0.5685
Epoch 1/1... Batch 2120... Discriminator Loss: 1.1684... Generator Loss: 0.7718
Epoch 1/1... Batch 2130... Discriminator Loss: 1.2483... Generator Loss: 0.8415
Epoch 1/1... Batch 2140... Discriminator Loss: 1.1547... Generator Loss: 0.9395
Epoch 1/1... Batch 2150... Discriminator Loss: 1.2051... Generator Loss: 0.7451
Epoch 1/1... Batch 2160... Discriminator Loss: 1.1688... Generator Loss: 1.0631
Epoch 1/1... Batch 2170... Discriminator Loss: 1.2748... Generator Loss: 0.7649
Epoch 1/1... Batch 2180... Discriminator Loss: 1.1699... Generator Loss: 1.0043
Epoch 1/1... Batch 2190... Discriminator Loss: 1.4289... Generator Loss: 1.4465
Epoch 1/1... Batch 2200... Discriminator Loss: 1.2484... Generator Loss: 1.0332
Epoch 1/1... Batch 2210... Discriminator Loss: 1.4497... Generator Loss: 0.6036
Epoch 1/1... Batch 2220... Discriminator Loss: 1.1537... Generator Loss: 1.1273
Epoch 1/1... Batch 2230... Discriminator Loss: 1.3424... Generator Loss: 0.6013
Epoch 1/1... Batch 2240... Discriminator Loss: 1.1575... Generator Loss: 0.8334
Epoch 1/1... Batch 2250... Discriminator Loss: 1.1435... Generator Loss: 1.3422
Epoch 1/1... Batch 2260... Discriminator Loss: 1.1351... Generator Loss: 1.1738
Epoch 1/1... Batch 2270... Discriminator Loss: 1.4093... Generator Loss: 1.2374
Epoch 1/1... Batch 2280... Discriminator Loss: 1.2355... Generator Loss: 1.3752
Epoch 1/1... Batch 2290... Discriminator Loss: 1.1190... Generator Loss: 1.0232
Epoch 1/1... Batch 2300... Discriminator Loss: 1.1195... Generator Loss: 1.2255
Epoch 1/1... Batch 2310... Discriminator Loss: 1.3188... Generator Loss: 0.7423
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Epoch 1/1... Batch 5780... Discriminator Loss: 1.0525... Generator Loss: 0.9999
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Epoch 1/1... Batch 5890... Discriminator Loss: 1.1350... Generator Loss: 0.8966
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Epoch 1/1... Batch 6310... Discriminator Loss: 1.2243... Generator Loss: 0.8308
Epoch 1/1... Batch 6320... Discriminator Loss: 1.1051... Generator Loss: 0.8334
Epoch 1/1... Batch 6330... Discriminator Loss: 1.2208... Generator Loss: 0.7660

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.